Recently, several deep learning-based methods have also been proposed for ECG biometrics. Such methods have demonstrated much greater accuracy in identification compared to the traditional approaches. Although such methods often rely on the detection of R-peaks for proper alignment of the signals, they still circumvent the flaws of the fiducial approaches. Since R-peak is the highest and sharpest point of ECG and the QRS complex is less likely to be affected by physical movement or emotion, they are popularly used for ECG signal alignment [1]}. Convolutional networks, ranging from vanilla architectures [1]}, multi-resolutional models [3]}, [4]}, residual models [5]} have all demonstrated superior accuracy in this context. Similarly, recurrent network models such as Long Short-Term Memory (LSTM) [6]} or Gated Recurrent Unit (GRU) [7]} have also been successfully applied for precise individual authentication.
